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1. Swarm Intelligence

2. Swarms
 Natural phenomena as inspiration
 A flock of birds sweeps across the Sky.
 How do ants collectively forage for food?
 How does a school of fish swims, turns together?
 They are so ordered.

3.  What made them to be so ordered?
 There is no centralized controller
 But they exhibit complex global behavior.
 Individuals follow simple rules to interact with
neighbors .
 Rules followed by birds
 collision avoidance
 velocity matching
 Flock Centering

4. Swarm Intelligence-Definition
 “Swarm intelligence (SI) is artificial intelligence
based on the collective behavior of decentralized,
self-organized systems”

5. Characteristics of Swarms
 Composed of many individuals
 Individuals are homogeneous
 Local interaction based on simple rules
 Self-organization

6. Overview
 Ant colony optimization
 TSP
 Bees Algorithms
 Comparison between bees and ants
 Conclusions

7. Ant Colony Optimization
 The way ants find their food in shortest path is
interesting.
 Ants secrete pheromones to remember their path.
 These pheromones evaporate with time.

8. Ant Colony Optimization..
 Whenever an ant finds food , it marks its return
journey with pheromones.
 Pheromones evaporate faster on longer paths.
 Shorter paths serve as the way to food for most of
the other ants.

9. Ant Colony Optimization
 The shorter path will be reinforced by the
pheromones further.
 Finally , the ants arrive at the shortest path.

10. Optimizations of SI
 Swarms have the ability to solve problems
 Ant Colony Optimization (ACO) , a meta-heuristic
 ACO can be used to solve hard problems like TSP,
Quadratic Assignment Problem(QAP)
 We discuss ACO meta-heuristic for TSP

11. ACO-TSP
 Given a graph with n nodes, should give the
shortest Hamiltonian cycle
 m ants traverse the graph
 Each ant starts at a random node

12. Transitions
 Ants leave pheromone trails when they make a
transition
 Trails are used in prioritizing transition

13. Transitions
 Suppose ant k is at u.
 Nk(u) be the nodes not visited by k
 Tuv be the pheromone trail of edge (u,v)
 k jumps from u to a node v in Nk(u) with
probability
puv(k) = Tuv ( 1/ d(u,v))

14. Iteration of AOC-TSP
 m ants are started at random nodes
 They traverse the graph prioritized on trails and
edge-weights
 An iteration ends when all the ants visit all nodes
 After each iteration, pheromone trails are updated.

15. Updating Pheromone trails
 New trail should have two components
 Old trail left after evaporation and
 Trails added by ants traversing the edge during the
iteration
 T'uv = (1-p) Tuv + ChangeIn(Tuv)
 Solution gets better and better as the number of
iterations increase

16. Performance of TSP with ACO heuristic
 Performs better than state-of-the-art TSP
algorithms for small (50-100) of nodes
 The main point to appreciate is that Swarms give
us new algorithms for optimization

17. Bee Algorithm

19. Bees Foraging
 Recruitment Behaviour :
 Waggle Dancing
 series of alternating left and right loops
 Direction of dancing
 Duration of dancing
 Navigation Behaviour :
 Path vector represents knowledge representation of
path by inspect
 Construction of PI.

20. Algorithm
 It has two steps :
 ManageBeesActivity()
 CalculateVectors()
 ManageBeesActivity: It handles agents activities
based on their internal state. That is it decides
action it has to take depending on the knowledge it
has.
 CalculateVectors : It is used for administrative
purposes and calculates PI vectors for the agents.

21. Uses of Bee Algorithm
 Training neural networks for pattern recognition
 Forming manufacturing cells.
 Scheduling jobs for a production machine.
 Data clustering

22. Comparisons
 Ants use pheromones for back tracking route to
food source.
 Bees instead use Path Integration. Bees are able to
compute their present location from past trajectory
continuously.
 So bees can return to home through direct route
instead of back tracking their original route.
 Does path emerge faster in this algorithm.

23. Results
 Experiments with different test cases on these
algorithms show that.
 Bees algorithm is more efficient when finding and
collecting food, that is it takes less number of steps.
 Bees algorithm is more scalable it requires less
computation time to complete task.
 Bees algorithm is less adaptive than ACO.

24. Applications of SI
 In Movies : Graphics in movies like Lord of the
Rings trilogy, Troy.
 Unmanned underwater vehicles(UUV):
 Groups of UUVs used as security units
 Only local maps at each UUV
 Joint detection of and attack over enemy vessels by co-
ordinating within the group of UUVs

25. More Applications
 Swarmcasting:
 For fast downloads in a peer-to-peer file-sharing
network
 Fragments of a file are downloaded from different
hosts in the network, parallelly.
 AntNet : a routing algorithm developed on the
framework of Ant Colony Optimization
 BeeHive : another routing algorithm modelled on
the communicative behaviour of honey bees

26. A Philosophical issue
 Individual agents in the group seem to have no
intelligence but the group as a whole displays
some intelligence
 In terms of intelligence, whole is not equal to sum
of parts?
 Where does the intelligence of the group come
from ?
 Answer : Rules followed by individual agents

27. Conclusion
 SI provides heuristics to solve difficult
optimization problems.
 Has wide variety of applications.
 Basic philosophy of Swarm Intelligence : Observe
the behaviour of social animals and try to mimic
those animals on computer systems.
 Basic theme of Natural Computing: Observe
nature, mimic nature.

28. Bibliography
 A Bee Algorithm for Multi-Agents System-
Lemmens ,Steven . Karl Tuyls, Ann Nowe -2007
 Swarm Intelligence – Literature Overview, Yang
Liu , Kevin M. Passino. 2000.
 www.wikipedia.org
 The ACO metaheuristic: Algorithms, Applications,
and Advances. Marco Dorigo and Thomas Stutzle-
Handbook of metaheuristics, 2002.